Render this report with
~/spinal_cord_paper/scripts/Gg_devel_scWGCNA_module_analysis_render.sh.
library(Seurat)
Attaching SeuratObject
library(WGCNA)
Loading required package: dynamicTreeCut
Loading required package: fastcluster
Attaching package: ‘fastcluster’
The following object is masked from ‘package:stats’:
hclust
Attaching package: ‘WGCNA’
The following object is masked from ‘package:stats’:
cor
library(tidyr)
library(ggplot2)
library(stringr)
library(patchwork)
library(tidyverse)
Registered S3 methods overwritten by 'dbplyr':
method from
print.tbl_lazy
print.tbl_sql
── Attaching packages ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── tidyverse 1.3.1 ──
✔ tibble 3.1.8 ✔ dplyr 1.0.10
✔ readr 1.4.0 ✔ forcats 0.5.1
✔ purrr 0.3.4
── Conflicts ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag() masks stats::lag()
library(cowplot)
Attaching package: ‘cowplot’
The following object is masked from ‘package:patchwork’:
align_plots
library(pheatmap)
library(gridExtra)
Attaching package: ‘gridExtra’
The following object is masked from ‘package:dplyr’:
combine
source("~/Neuraltube/scripts/heatmap4.R")
Load individual seurat and test WGCNA data
The individual data sets are the Day 5 (Gg_D05_ctrl_seurat_070323),
Day 7 (Gg_D07_ctrl_seurat_070323), and Day 10 (Gg_ctrl_1_seurat_070323)
chicken spinal cord sets. The test WGCNA data are the modules calculated
on the integrated data set of all three stages.
se_path <- c("Gg_D05_ctrl_seurat_070323",
"Gg_D07_ctrl_seurat_070323",
"Gg_ctrl_1_seurat_070323")
clust_col <- read.csv("~/spinal_cord_paper/annotations/broad_cluster_marker_colors.csv") %>%
rename(broad = broad_cluster) %>%
select(-marker)
Order of the broad clusters for plotting purposes.
broad_order <- c("progenitors",
"FP",
"RP",
"FP/RP",
"neurons",
"OPC",
"MFOL",
"pericytes",
"microglia",
"blood",
"vasculature"
)
my.ses <- list()
col_table <- list()
ord_levels <- list()
for (i in seq(se_path)) {
# load the data sets
my.se <- readRDS(paste0("~/spinal_cord_paper/data/", se_path[i], ".rds"))
annot <- read.csv(list.files("~/spinal_cord_paper/annotations",
pattern = str_remove(se_path[i], "_seurat_\\d{6}"),
full.names = TRUE))
if(length(table(annot$number)) != length(table(my.se$seurat_clusters))) {
stop("Number of clusters must be identical!")
}
# rename for left join
annot <- annot %>%
mutate(fine = paste(fine, number, sep = "_")) %>%
mutate(number = factor(number, levels = 1:nrow(annot))) %>%
rename(seurat_clusters = number)
# cluster order for vln plots
ord_levels[[i]] <- annot$fine[order(match(annot$broad, broad_order))]
# create index for color coding
col_table[[i]] <- annot %>%
left_join(clust_col, by = "broad") %>%
select(c("fine", "color"))
# add cluster annotation to meta data
my.se@meta.data <- my.se@meta.data %>%
rownames_to_column("rowname") %>%
left_join(annot, by = "seurat_clusters") %>%
mutate(fine = factor(fine, levels = annot$fine)) %>%
column_to_rownames("rowname")
my.ses[[i]] <- my.se
}
names(my.ses) <- c("D05", "D07", "D10")
names(col_table) <- c("D05", "D07", "D10")
names(ord_levels) <- c("D05", "D07", "D10")
rm(my.se, annot)
# The reference WGCNA data. We can have several, if we want to test many at the same time
WGCNA_data = list()
WGCNA_data[[1]] = readRDS("~/spinal_cord_paper/output/Gg_devel_int_scWGCNA_250723.rds")
my.wsub =list()
my.wsub[[1]]= c(1:22)
# the name of each sample, as they appear in my.files and in the metadata of the combined object
my.samplenames = c("D05", "D07", "D10")
# This is just to add a little bit more sense to the modules, so that we don't get just a color. Corresponds to WGCNA_data
my.modulenames = list()
my.modulenames[[1]] = c(1:22)
Module gene correlation
Here, we do a correlation matrix / heatmap, to see which cell
clusters group togheter. This helps us to make more detailed
dotplots.
This part of the script can still be used to compare several WGCNA
datasets in parallel.
# broad cluster color table
all_col <- do.call(rbind, col_table) %>%
rownames_to_column("sample") %>%
mutate(sample = substr(sample, 1, 3)) %>%
mutate(sample_celltype = paste(sample, fine, sep = "_")) %>%
select(c("color", "sample_celltype", "sample"))
Average expression data
cell_table <- my.metam %>%
rownames_to_column("cell_ID") %>%
select("sample_celltype", "cell_ID")
avg.mod.eigengenes <- WGCNA_data[[1]]$sc.MEList$averageExpr %>%
rownames_to_column("cell_ID") %>%
left_join(cell_table, by = "cell_ID") %>%
column_to_rownames("cell_ID")
# add metadata
avg.mod.eigengenes.mean <- avg.mod.eigengenes %>%
group_by(sample_celltype) %>%
summarise_all("mean") %>%
column_to_rownames("sample_celltype")
spearman correlation heatmap
annotations
#The colors for the samples and clusters. First the closest to the heatmap. Add a white space to easy the eye and make less confussing
my.heatcols <- as.matrix(data.frame(
sample= as.character(my.colsm[match(all_col$sample, names(my.colsm))]),
"." = "white",
cluster= as.character(all_col$color[match(rownames(avg.mod.eigengenes.mean), all_col$sample_celltype)]))
)
# names and colors for the heatmap annotation
annot_name <- data.frame(
"Celltypes" = all_col$sample_celltype,
"Sample" = all_col$sample,
row.names = all_col$sample_celltype)
annot_module <- data.frame(
"Module" = colnames(avg.mod.eigengenes)[1:22],
row.names = colnames(avg.mod.eigengenes)[1:22]
)
pheat_col_table <- do.call(rbind, col_table) %>%
rownames_to_column("sample") %>%
mutate(sample = substr(sample, 1,3)) %>%
mutate(fine = paste(sample, fine, sep = "_"))
# match color table with annotation
pheat_col_table <- pheat_col_table[match(annot_name$Celltypes, pheat_col_table$fine),]
annot_col <- list(
Celltypes = pheat_col_table$color,
Sample = c(D05 = "#A4A4A4",
D07 = "#515151",
D10 = "#000000"),
Module = str_remove(colnames(avg.mod.eigengenes)[1:22], "AE")
)
names(annot_col[[1]]) <- annot_name$Celltypes
names(annot_col[[3]]) <- colnames(avg.mod.eigengenes)[1:22]
heat_col <- colorRampPalette(colors = c("dodgerblue4","dodgerblue", "white", "red", "darkred"))
#Calculate the distance 1-cor, and then calculate dendograms to cluster the clusters
my.hclust = hclust(as.dist(1-cor(t(avg.mod.eigengenes.mean), method = "spearman")))
# lower limit to scale color bar
low_limit <- 100 - abs(round(range(cor(t(avg.mod.eigengenes.mean), method = "spearman"))[1]*100))
pheatmap(cor(t(avg.mod.eigengenes.mean), method = "spearman"),revC = T,
main = "spearman correlation of average module eigengene expression by cluster\n Dendrogram based on 'distance 1-cor'",
fontsize = 8,
color = heat_col(200)[low_limit:200],
show_colnames = F,
cluster_rows = my.hclust,
cluster_cols = my.hclust,
treeheight_row = 0,
annotation_col = annot_name,
annotation_colors = annot_col,
annotation_legend = F,
border_color = NA
)
colbar <- c(min(cor(t(avg.mod.eigengenes.mean), method = "spearman")),
max(cor(t(avg.mod.eigengenes.mean), method = "spearman")))
#Calculate the distance 1-cor, and then calculate dendograms to cluster the clusters
my.hclust = as.dendrogram(hclust(as.dist(1-cor(t(avg.mod.eigengenes.mean), method = "spearman"))))
#Plot!
pdf("~/spinal_cord_paper/figures/Fig_1_module_gene_heatmap_spearman.pdf", width = 11, height = 11)
heatmap.4(cor(t(avg.mod.eigengenes.mean), method = "spearman"),
col = colorRampPalette(c("dodgerblue4","dodgerblue", "white", "red", "darkred"))(n = 1000),
trace="none",
lhei=c(0.2,1.6),
lwid=c(0.2,1.6),
scale = "none",margins = c(10,10),
cexCol = 0.75,
revC = TRUE,
ColSideColors = my.heatcols,
RowSideColors = t(my.heatcols[,3:1]),Rowv = my.hclust, Colv = my.hclust)
pdf("~/spinal_cord_paper/figures/Fig_1_module_gene_heatmap_color_key_spearman.pdf", width = 10, height = 10)
# heatmap since color key is missing in first heatmap
heatmap.4(rbind(colbar, colbar),
col = colorRampPalette(c("dodgerblue4","dodgerblue", "white", "red", "darkred"))(n = 1000),
trace="none",
lhei=c(0.2,1.6),
lwid=c(0.2,1.6),
scale = "none",margins = c(10,10),
cexCol = 0.75)

heatmap of module pseudobulk average expression
# module colors
my.colcols = as.matrix(names(table(WGCNA_data[[1]]$dynamicCols)))
htmp <- pheatmap(as.matrix(avg.mod.eigengenes.mean),
fontsize = 8,
scale = "column",
color = colorRampPalette(c("#4d2d87","#7c55a3", "white", "#ed9921", "#895d25"))(n = 1000),
annotation_row = annot_name,
annotation_col = annot_module,
annotation_colors = annot_col,
annotation_legend = F,
border_color = NA)
Error in grid.newpage() :
could not open file '/scicore/home/tschoppp/sacfab01/.local/share/rstudio/notebooks/7247CC19-Gg_devel_scWGCNA_module_analysis/1/6B3D10EC1f9f2767/coa2rns2f0mbr_t/_rs_chunk_plot_002.png'
Load the integrated data set
The integrated data set on which the WGCNA is calculated. We plot it,
split by the original cell types from the three original samples.
# This is a file of all the combined mouse datasets, normalized and such.
my.sec = readRDS("~/spinal_cord_paper/data/Gg_devel_int_seurat_250723.rds")
identical(rownames(my.metam), colnames(my.sec))
[1] TRUE
my.metam$sample_celltype <- factor(my.metam$sample_celltype, levels = all_col$sample_celltype)
#Set the identities of the integrated data, to the annotated clusters
my.sec = SetIdent(my.sec, value = my.metam$sample_celltype)
p1 <- DimPlot(
my.sec,
reduction = "tsne",
label = TRUE,
repel = TRUE,
cols = all_col$color,
split.by = "orig.ident"
) +
NoLegend()
p1
pdf("~/spinal_cord_paper/figures/Devel_split_tsne.pdf", height = 7, width = 15)
#Plot split tsne
p1

Avg. module exp. by stage
tSNE DimPlots showing the average expression of each module by
stage.

AE over time
We plot the average expression of each module in the three stages and
the 5 broad cell type clusters present in all 3 stages.
VlnPlots of avg. module exp. by stage and seurat cluster
colored by module
# reorder seurat clusters
for (i in seq(my.ses)) {
my.ses[[i]]$seurat_clusters <- factor(
my.ses[[i]]$seurat_clusters,
levels = levels(my.ses[[i]]$seurat_clusters)[as.integer(str_extract(ord_levels[[i]], "\\d{1,2}$"))]
)
}
vplots <- list()
for (i in seq(my.ses)) {
mods <- colnames(my.ses[[i]][[]])[grep("^AE",colnames(my.ses[[i]][[]]))]
vplots[[i]] <- VlnPlot(
my.ses[[i]],
features = mods[module_order],
group.by = "seurat_clusters",
stack = TRUE, flip = TRUE,
cols = substring(mods, 3)[module_order]) +
theme(legend.position = "none") +
geom_hline(yintercept = 0, lty = "dashed")
}
vplots[[1]]
vplots[[2]]
vplots[[3]]
pdf("~/spinal_cord_paper/figures/Fig_2_AE_by_cluster_modcol.pdf", height = 20, width = 10)
vplots[[1]]
vplots[[2]]
vplots[[3]]
colored by cell type
clust_col <- read.csv("~/spinal_cord_paper/annotations/broad_cluster_marker_colors.csv")
vplots_id <- list()
for (i in seq(my.ses)) {
mods <- colnames(my.ses[[i]][[]])[grep("^AE",colnames(my.ses[[i]][[]]))]
vplots_id[[i]] <- VlnPlot(
my.ses[[i]],
features = mods[module_order],
group.by = "seurat_clusters",
stack = TRUE, flip = TRUE,
fill.by = "ident",
cols = col_table[[i]]$color[as.integer(str_extract(ord_levels[[i]], "\\d{1,2}$"))]) +
theme(legend.position = "none") +
geom_hline(yintercept = 0, lty = "dashed")
}
vplots_id[[1]]
vplots_id[[2]]
vplots_id[[3]]
pdf("~/spinal_cord_paper/figures/Fig_2_AE_by_cluster_clucol.pdf", height = 20, width = 10)
vplots_id[[1]]
vplots_id[[2]]
vplots_id[[3]]
MN modules
For the figures we specifically select the two MN modules and plot
them as Vln plots.

VlnPlot(
my.sec,
features = mods[module_order],
group.by = "sample_celltype",
stack = TRUE, flip = TRUE,
cols = substring(mods, 3)[module_order]) +
theme(legend.position = "none") +
geom_hline(yintercept = 0, lty = "dashed")
pdf("~/spinal_cord_paper/figures/Fig_2_AE_by_cluster_integrated_data.pdf", height = 20, width = 30)
VlnPlot(
my.sec,
features = mods[module_order],
group.by = "sample_celltype",
stack = TRUE, flip = TRUE,
cols = substring(mods, 3)[module_order]) +
theme(legend.position = "none") +
geom_hline(yintercept = 0, lty = "dashed")
```r
# Date and time of Rendering
Sys.time()
sessionInfo()
```
---
title: "Devel_int WGCNA modules expression in NT D5, D7, and D10 samples"
author: "Fabio Sacher"
date: "04.08.2023"
output:
  html_document:
    toc: TRUE
    toc_float: TRUE
  html_notebook:
    fig_height: 7
    fig_width: 8
editor_options:
  chunk_output_type: inline
---

Render this report with ~/spinal_cord_paper/scripts/Gg_devel_scWGCNA_module_analysis_render.sh.

```{r setup}
library(Seurat)
library(WGCNA)
library(tidyr)
library(ggplot2)
library(stringr)
library(patchwork)
library(tidyverse)
library(cowplot)
library(pheatmap)
library(gridExtra)
source("~/Neuraltube/scripts/heatmap4.R")

```

# Load individual seurat and test WGCNA data

The individual data sets are the Day 5 (Gg_D05_ctrl_seurat_070323), Day 7 (Gg_D07_ctrl_seurat_070323), and Day 10 (Gg_ctrl_1_seurat_070323) chicken spinal cord sets. The test WGCNA data are the modules calculated on the integrated data set of all three stages.

```{r data-sets}
se_path <- c("Gg_D05_ctrl_seurat_070323",
             "Gg_D07_ctrl_seurat_070323",
             "Gg_ctrl_1_seurat_070323")

clust_col <- read.csv("~/spinal_cord_paper/annotations/broad_cluster_marker_colors.csv") %>% 
  rename(broad = broad_cluster) %>% 
  select(-marker)

```

Order of the broad clusters for plotting purposes.

```{r ordering}
broad_order <- c("progenitors",
      "FP",
      "RP",
      "FP/RP",
      "neurons",
      "OPC",
      "MFOL",
      "pericytes",
      "microglia",
      "blood",
      "vasculature"
      )
```


```{r seurat-objects-and-annotations}
my.ses <- list()
col_table <- list()
ord_levels <- list()

for (i in seq(se_path)) {
  # load the data sets
  my.se <- readRDS(paste0("~/spinal_cord_paper/data/", se_path[i], ".rds"))
  annot <- read.csv(list.files("~/spinal_cord_paper/annotations",
                               pattern = str_remove(se_path[i], "_seurat_\\d{6}"),
                               full.names = TRUE))
  
  if(length(table(annot$number)) != length(table(my.se$seurat_clusters))) {
     stop("Number of clusters must be identical!")
  }
  
  # rename for left join
  annot <- annot %>% 
    mutate(fine = paste(fine, number, sep = "_")) %>% 
    mutate(number = factor(number, levels = 1:nrow(annot))) %>% 
    rename(seurat_clusters = number) 
  
  # cluster order for vln plots
  ord_levels[[i]] <- annot$fine[order(match(annot$broad, broad_order))]
  
  # create index for color coding
  col_table[[i]] <- annot %>%
    left_join(clust_col, by = "broad") %>% 
    select(c("fine", "color"))
  
  # add cluster annotation to meta data
  my.se@meta.data <- my.se@meta.data %>% 
    rownames_to_column("rowname") %>% 
    left_join(annot, by = "seurat_clusters") %>% 
    mutate(fine = factor(fine, levels = annot$fine)) %>% 
    column_to_rownames("rowname")
  
  my.ses[[i]] <- my.se

}

names(my.ses) <- c("D05", "D07", "D10")
names(col_table) <- c("D05", "D07", "D10")
names(ord_levels) <- c("D05", "D07", "D10")

rm(my.se, annot)
```

```{r input data, echo=TRUE}
# The reference WGCNA data. We can have several, if we want to test many at the same time
WGCNA_data = list()
WGCNA_data[[1]] = readRDS("~/spinal_cord_paper/output/Gg_devel_int_scWGCNA_250723.rds")
my.wsub =list()
my.wsub[[1]]= c(1:22)

# the name of each sample, as they appear in my.files and in the metadata of the combined object
my.samplenames = c("D05", "D07", "D10")

# This is just to add a little bit more sense to the modules, so that we don't get just a color. Corresponds to WGCNA_data
my.modulenames = list()
my.modulenames[[1]] = c(1:22)

```

# Module gene correlation

Here, we do a correlation matrix / heatmap, to see which cell clusters group togheter. This helps us to make more detailed dotplots.  
This part of the script can still be used to compare several WGCNA datasets in parallel.  

```{r}
# broad cluster color table
all_col <- do.call(rbind, col_table) %>% 
  rownames_to_column("sample") %>% 
  mutate(sample = substr(sample, 1, 3)) %>% 
  mutate(sample_celltype = paste(sample, fine, sep = "_")) %>% 
  select(c("color", "sample_celltype", "sample"))
```

# Meta data

```{r meta-data}
#Get a dataframe with annotations for all the samples and colors we need.
my.metam <- list()

for (i in seq(my.ses)) {
  my.metam[[i]] <- my.ses[[i]][[]]
  rownames(my.metam[[i]]) <- paste0(rownames(my.metam[[i]]), "_", i)
}
my.metam <- do.call(rbind, my.metam)

my.metam$orig.ident <- str_replace_all(my.metam$orig.ident, pattern =  "Gg_D05_ctrl", "D05")
my.metam$orig.ident <- str_replace_all(my.metam$orig.ident, pattern =  "Gg_D07_ctrl", "D07")
my.metam$orig.ident <- str_replace_all(my.metam$orig.ident, pattern =  "Gg_ctrl_1", "D10")

# my.metam$sample_celltype = paste0(substr(my.metam$orig.ident,7,9),"_",my.metam$seurat_clusters)
my.metam$sample_celltype = paste0(my.metam$orig.ident, "_", my.metam$fine)

my.metam <- my.metam %>% 
  tibble::rownames_to_column(var = "cell_ID") %>%
  dplyr::left_join(all_col, by = "sample_celltype") %>%
  tibble::column_to_rownames(var = "cell_ID")


# get sample colors
my.colsm = c("grey", "grey30", "black")
names(my.colsm) <- c("D05", "D07", "D10")
 
```


# Average expression data

```{r}

cell_table <- my.metam %>% 
  rownames_to_column("cell_ID") %>% 
  select("sample_celltype", "cell_ID")

avg.mod.eigengenes <- WGCNA_data[[1]]$sc.MEList$averageExpr %>% 
  rownames_to_column("cell_ID") %>% 
  left_join(cell_table, by = "cell_ID") %>% 
  column_to_rownames("cell_ID")
  

# add metadata

avg.mod.eigengenes.mean <- avg.mod.eigengenes %>%
  group_by(sample_celltype) %>%
  summarise_all("mean") %>%
  column_to_rownames("sample_celltype")

```

## spearman correlation heatmap

### annotations

```{r annot-list}

#The colors for the samples and clusters. First the closest to the heatmap. Add a white space to easy the eye and make less confussing
my.heatcols <- as.matrix(data.frame(
    sample= as.character(my.colsm[match(all_col$sample, names(my.colsm))]),
    "." = "white",
    cluster= as.character(all_col$color[match(rownames(avg.mod.eigengenes.mean), all_col$sample_celltype)]))
    )


# names and colors for the heatmap annotation
annot_name <- data.frame(
  "Celltypes" = all_col$sample_celltype,
  "Sample"    = all_col$sample,
  row.names = all_col$sample_celltype)
  

annot_module <- data.frame(
  "Module" = colnames(avg.mod.eigengenes)[1:22],
  row.names = colnames(avg.mod.eigengenes)[1:22]
)

pheat_col_table <- do.call(rbind, col_table) %>% 
  rownames_to_column("sample") %>% 
  mutate(sample = substr(sample, 1,3)) %>% 
  mutate(fine = paste(sample, fine, sep = "_"))

# match color table with annotation
pheat_col_table <- pheat_col_table[match(annot_name$Celltypes, pheat_col_table$fine),]

annot_col <- list(
  Celltypes = pheat_col_table$color,
  Sample = c(D05 = "#A4A4A4",
             D07 = "#515151",
             D10 = "#000000"),
  Module = str_remove(colnames(avg.mod.eigengenes)[1:22], "AE")
  )

names(annot_col[[1]]) <- annot_name$Celltypes
names(annot_col[[3]]) <- colnames(avg.mod.eigengenes)[1:22]

```

```{r,  fig.height=10, fig.width=13}
heat_col <- colorRampPalette(colors = c("dodgerblue4","dodgerblue", "white", "red", "darkred"))

#Calculate the distance 1-cor, and then calculate dendograms to cluster the clusters
my.hclust = hclust(as.dist(1-cor(t(avg.mod.eigengenes.mean), method = "spearman")))

# lower limit to scale color bar
low_limit <- 100 - abs(round(range(cor(t(avg.mod.eigengenes.mean), method = "spearman"))[1]*100))

pheatmap(cor(t(avg.mod.eigengenes.mean), method = "spearman"),revC = T,
                 main = "spearman correlation of average module eigengene expression by cluster\n Dendrogram based on 'distance 1-cor'",
                 fontsize = 8,  
                 color = heat_col(200)[low_limit:200],
                 show_colnames = F,
                 cluster_rows = my.hclust,
                 cluster_cols = my.hclust,
                 treeheight_row = 0, 
                 annotation_col = annot_name,
                 annotation_colors = annot_col,
                 annotation_legend = F,
                 border_color = NA
                 )

colbar <- c(min(cor(t(avg.mod.eigengenes.mean), method = "spearman")),
            max(cor(t(avg.mod.eigengenes.mean), method = "spearman")))

#Calculate the distance 1-cor, and then calculate dendograms to cluster the clusters
my.hclust = as.dendrogram(hclust(as.dist(1-cor(t(avg.mod.eigengenes.mean), method = "spearman"))))

#Plot!
pdf("~/spinal_cord_paper/figures/Fig_1_module_gene_heatmap_spearman.pdf", width = 11, height = 11)
heatmap.4(cor(t(avg.mod.eigengenes.mean), method = "spearman"),
          col = colorRampPalette(c("dodgerblue4","dodgerblue", "white", "red", "darkred"))(n = 1000),
          trace="none",
          lhei=c(0.2,1.6),
          lwid=c(0.2,1.6),
          scale = "none",margins = c(10,10),
          cexCol = 0.75,
          revC = TRUE,
          ColSideColors = my.heatcols,
          RowSideColors = t(my.heatcols[,3:1]),Rowv = my.hclust, Colv = my.hclust)


pdf("~/spinal_cord_paper/figures/Fig_1_module_gene_heatmap_color_key_spearman.pdf", width = 10, height = 10)
# heatmap since color key is missing in first heatmap
heatmap.4(rbind(colbar, colbar),
          col = colorRampPalette(c("dodgerblue4","dodgerblue", "white", "red", "darkred"))(n = 1000),
          trace="none",
          lhei=c(0.2,1.6),
          lwid=c(0.2,1.6),
          scale = "none",margins = c(10,10),
          cexCol = 0.75)

```

## heatmap of module pseudobulk average expression

```{r,  fig.height=15, fig.width=10}
# module colors
my.colcols = as.matrix(names(table(WGCNA_data[[1]]$dynamicCols)))

htmp <- pheatmap(as.matrix(avg.mod.eigengenes.mean),
         fontsize = 8,
         scale = "column",
         color = colorRampPalette(c("#4d2d87","#7c55a3", "white", "#ed9921", "#895d25"))(n = 1000),
         annotation_row = annot_name,
         annotation_col = annot_module,
         annotation_colors = annot_col,
         annotation_legend = F,
         border_color = NA)

module_order <- htmp[["tree_col"]]$order

pdf("~/spinal_cord_paper/figures/Fig_1_devel_module_v_clusters_heatmap.pdf", width = 7, height = 10)
pheatmap(as.matrix(avg.mod.eigengenes.mean),
         fontsize = 8,
         scale = "column",
         color = colorRampPalette(c("#4d2d87","#7c55a3", "white", "#ed9921", "#895d25"))(n = 1000),
         annotation_row = annot_name,
         annotation_col = annot_module,
         annotation_colors = annot_col,
         annotation_legend = F,
         border_color = NA)


```



# Load the integrated data set

The integrated data set on which the WGCNA is calculated. We plot it, split by the original cell types from the three original samples. 

```{r dimplots, fig.width=10}
# This is a file of all the combined mouse datasets, normalized and such.
my.sec = readRDS("~/spinal_cord_paper/data/Gg_devel_int_seurat_250723.rds")

identical(rownames(my.metam), colnames(my.sec))

my.metam$sample_celltype <- factor(my.metam$sample_celltype, levels = all_col$sample_celltype)
#Set the identities of the integrated data, to the annotated clusters
my.sec = SetIdent(my.sec, value = my.metam$sample_celltype)

p1 <- DimPlot(
  my.sec,
  reduction = "tsne",
  label = TRUE,
  repel = TRUE,
  cols = all_col$color,
  split.by = "orig.ident"
  ) + 
  NoLegend()

p1

pdf("~/spinal_cord_paper/figures/Devel_split_tsne.pdf", height = 7, width = 15)
#Plot split tsne
p1
```

# Avg. module exp. by stage

tSNE DimPlots showing the average expression of each module by stage.

```{r AE, message = FALSE, fig.width=12, fig.height=40}

for (i in seq(my.ses)) {
  # prepare average expression table
  tmp <- avg.mod.eigengenes[,1:22] %>%
    tibble::rownames_to_column("cell_ID") %>%
    dplyr::filter(grepl(paste0("_", i, "$"), cell_ID)) %>%
    dplyr::mutate(cell_ID = stringr::str_remove_all(cell_ID, paste0("_", i))) %>%
    tibble::column_to_rownames("cell_ID")
  
  identical(rownames(tmp), colnames(my.ses[[i]]))
  # add meta data to the seurat objects
  my.ses[[i]] <- AddMetaData(my.ses[[i]], tmp)
}

#max and min expression per module (column max)
mod_max <- apply(avg.mod.eigengenes[,1:22], MARGIN = 2, FUN = max)[module_order]
mod_min <- apply(avg.mod.eigengenes[,1:22], MARGIN = 2, FUN = min)[module_order]

modplots <- list()
modplots[[1]] <- list()
modplots[[2]] <- list()
modplots[[3]] <- list()

modules_in_order <- colnames(tmp)[module_order]

# plot the modules split to the stages
for (i in seq(my.ses)) {
  for (j in seq(ncol(tmp))) {
  
    modplots[[i]][[j]]  <- FeaturePlot(
      my.ses[[i]], order = TRUE,
      features = modules_in_order[j],
      reduction = "tsne"
      ) +
      ggtitle(stringr::str_remove(modules_in_order[j],"^AE")) +
      scale_color_gradient(low="ivory2", high=substring(modules_in_order[j], 3), #colors in the scale
                 limits=c(mod_min[j], mod_max[j])) #same limits for plots

    
    }
}

full_plot <- c(modplots[[1]], modplots[[2]], modplots[[3]])

gridExtra::grid.arrange(grobs = full_plot, ncol = 3, as.table = FALSE)

pdf("~/spinal_cord_paper/figures/Fig_2_devel_modules_AE_plots.pdf", width = 12, height = 70)
gridExtra::grid.arrange(grobs = full_plot, ncol = 3, as.table = FALSE)

pdf("~/spinal_cord_paper/figures/Supp_Fig_2_modules_darkgreen_AE_plots.pdf", width = 13, height = 4)
(full_plot[[9]] + full_plot[[31]] + full_plot[[53]]) + plot_layout(ncol = 3, guides = "collect")
```

# AE over time

We plot the average expression of each module in the three stages and the 5 broad cell type clusters present in all 3 stages.

```{r AE-over-time, fig.height=7, fig.width=12}
# module annotations
mod_annot <- read.csv("~/spinal_cord_paper/annotations/Gg_devel_int_scWGCNA_module_annotation.csv") %>%
  dplyr::mutate(module = str_replace_all(module, "\\d{1,2}\\_", "AE"))

meta <- list()

for (i in seq(my.ses)) {
  meta[[i]] <- my.ses[[i]]@meta.data %>%
    tibble::rownames_to_column("cell_ID") %>%
    dplyr::mutate(cell_ID = paste0(cell_ID, "_", i)) %>%
    dplyr::select(c("cell_ID", "broad"))
}

meta <- do.call(rbind, meta)

# mean average expression by stage
mean_AE <- avg.mod.eigengenes[,1:22] %>%
  tibble::rownames_to_column("cell_ID") %>%
  dplyr::mutate(stage = stringr::str_sub(cell_ID, -1)) %>%
  dplyr::mutate(stage = factor(stage, levels = c(1:3), labels = c("D05", "D07", "D10"))) %>%
  dplyr::left_join(meta, by = "cell_ID") %>%
  tibble::column_to_rownames("cell_ID") %>%
  tidyr::unite("stage_cl", stage, broad, sep = "_") %>%
  dplyr::group_by(stage_cl) %>%
  dplyr::summarise_each(mean) %>%
  dplyr::ungroup() %>%
  gather(key="module", value = "AE", -stage_cl) %>%
  dplyr::left_join(mod_annot[, c(1,3)], by = "module") %>%
  tidyr::separate("stage_cl", c("stage", "broad"), sep = "_", remove = FALSE) 

labels_dotplot <- stringr::str_remove(modules_in_order, "^AE")
names(labels_dotplot) <- modules_in_order

plot_clusters <- c("progenitors", "neurons", "RP", "FP", "pericytes", "OPC", "MFOL", "microglia", "blood")

mean_mod <- ggplot(data = mean_AE,
  aes(
    x = stage,
    y = AE,
    color = factor(broad, levels = plot_clusters),
    group = broad,
    label = annotation
    )
  ) +
  geom_line() +
  geom_point() +
  scale_color_manual(values = clust_col$color[match(plot_clusters, clust_col$broad)]) +
  theme_bw() +
  # facet wrap with reordered factors
  facet_wrap(vars(factor(module, levels = unique(mean_AE$module)[module_order])),
             scales = "free_y",
             nrow = 4,
             ncol = 6) +
  labs(color = "broad") +
  ggtitle("Average module expression by stage")

plotly::ggplotly(mean_mod)

pdf("~/spinal_cord_paper/figures/Fig_2_mean_mod_AE.pdf", height = 7, width = 12)
#Plot split tsne
mean_mod
```

# VlnPlots of avg. module exp. by stage and seurat cluster 

## colored by module

```{r average-module-expression-per-cluster, fig.width=10, fig.height=20}

# reorder seurat clusters
for (i in seq(my.ses)) {
  my.ses[[i]]$seurat_clusters <- factor(
  my.ses[[i]]$seurat_clusters,
  levels = levels(my.ses[[i]]$seurat_clusters)[as.integer(str_extract(ord_levels[[i]], "\\d{1,2}$"))]
  )

}

vplots <- list()

for (i in seq(my.ses)) {
  
  mods <- colnames(my.ses[[i]][[]])[grep("^AE",colnames(my.ses[[i]][[]]))]
  
  vplots[[i]] <- VlnPlot(
        my.ses[[i]],
        features = mods[module_order],
        group.by = "seurat_clusters",
        stack = TRUE, flip = TRUE,
        cols = substring(mods, 3)[module_order]) +
    theme(legend.position = "none") +
    geom_hline(yintercept = 0, lty = "dashed")
  
}

vplots[[1]]
vplots[[2]]
vplots[[3]]

pdf("~/spinal_cord_paper/figures/Fig_2_AE_by_cluster_modcol.pdf", height = 20, width = 10)
vplots[[1]]
vplots[[2]]
vplots[[3]]
```

## colored by cell type

```{r vln-by-cluster, fig.width=10, fig.height=20}
clust_col <- read.csv("~/spinal_cord_paper/annotations/broad_cluster_marker_colors.csv")

vplots_id <- list()

for (i in seq(my.ses)) {
    
  mods <- colnames(my.ses[[i]][[]])[grep("^AE",colnames(my.ses[[i]][[]]))]
  
  vplots_id[[i]] <- VlnPlot(
    my.ses[[i]],
    features = mods[module_order],
    group.by = "seurat_clusters",
    stack = TRUE, flip = TRUE,
    fill.by = "ident",
    cols = col_table[[i]]$color[as.integer(str_extract(ord_levels[[i]], "\\d{1,2}$"))]) +
    theme(legend.position = "none") +
    geom_hline(yintercept = 0, lty = "dashed")
}

vplots_id[[1]]
vplots_id[[2]]
vplots_id[[3]]

pdf("~/spinal_cord_paper/figures/Fig_2_AE_by_cluster_clucol.pdf", height = 20, width = 10)
vplots_id[[1]]
vplots_id[[2]]
vplots_id[[3]]
```

# MN modules

For the figures we specifically select the two MN modules and plot them as Vln plots.

```{r, fig.width=20, fig.height=5}
tmp <- avg.mod.eigengenes 
  
identical(rownames(tmp), colnames(my.sec))
# add meta data to the int seurat object
my.sec <- AddMetaData(my.sec, tmp)
my.sec <- AddMetaData(my.sec, my.metam[c("fine", "sample_celltype")])

custom_order <- c(paste(names(ord_levels)[1], ord_levels[[1]], sep = '_'),
                  paste(names(ord_levels)[2], ord_levels[[2]], sep = '_'),
                  paste(names(ord_levels)[3], ord_levels[[3]], sep = '_'))

my.sec$sample_celltype <- factor(
  my.sec$sample_celltype,
  levels = custom_order
  )

vln_ind <- VlnPlot(my.sec,
                   features = c("AEdarkred", "AElightgreen", "AEdarkgreen"),
                   group.by = "sample_celltype",
                   stack = TRUE,
                   flip = TRUE,
                   cols = c("darkred", "lightgreen", "darkgreen"),pt.size = 1
                  ) +
    NoLegend()

vln_ind
pdf("~/spinal_cord_paper/figures/Fig_2_AE_selected_mod.pdf", height = 22, width = 20)
vln_ind
```

```{r}
VlnPlot(
    my.sec,
    features = mods[module_order],
    group.by = "sample_celltype",
    stack = TRUE, flip = TRUE,
    cols = substring(mods, 3)[module_order]) +
    theme(legend.position = "none") +
    geom_hline(yintercept = 0, lty = "dashed")



pdf("~/spinal_cord_paper/figures/Fig_2_AE_by_cluster_integrated_data.pdf", height = 20, width = 30)
VlnPlot(
    my.sec,
    features = mods[module_order],
    group.by = "sample_celltype",
    stack = TRUE, flip = TRUE,
    cols = substring(mods, 3)[module_order]) +
    theme(legend.position = "none") +
    geom_hline(yintercept = 0, lty = "dashed")


```

```{r}
# Date and time of Rendering
Sys.time()

sessionInfo()
```

